Sea Surface Rainfall Detection and Intensity Retrieval Based on GNSS-Reflectometry Data From the CYGNSS Mission

Abstract

Rainfall detection (RD) and rainfall intensity (RI) retrieval are hot topics in the field of ocean remote sensing (RS). In the past, the sea surface RD and RI retrieval were usually based on X-band ocean radar image data. In this study, we aim to investigate the potential of global navigation satellite system-reflectometry (GNSS-R) for sea surface RD and RI retrieval based on delay Doppler maps (DDMs) data collected by the cyclone GNSS (CYGNSS) mission. First, the block-matching and 3-D filtering (BM3D) algorithm is proposed to improve the quality of DDM data. In addition, 12 GNSS-R observables derived from DDM are calculated, and an RD method based on the threshold of 12 GNSS-R observables is proposed based on the probability density function (PDF). When rainfall DDM data are detected, these data are used to develop and verify the sea surface RI retrieval model. The integrated multisatellite retrievals of global precipitation measurements (GPM-IMERG) data product are used as reference data to evaluate the performance of RD and RI retrieval model. The experimental results show that under very low wind speed (<5 m/s), the proposed trailing edge waveform summation of normalized integral delay waveform (TEWS-NIDW), TEWS of normalized center delay waveform (TEWS-NCDW), and TEWS of differential delay waveform (TEWS-DDW) observables are the best for RD, and the probability of detection of rainfall (PDr) is better than 75%. In the aspect of model retrieval performance, the root mean square error (RMSE) of the 12 observables is less than 4.66 mm/hr. Among them, the model based on TEWS-NCDW observables has the highest accuracy, better than 3.74 mm/hr.